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An opportunity solution tree connects a desired outcome to opportunities, solutions, and experiments. You define a measurable outcome, and the skill generates a hierarchical tree with multiple solution options and assumption tests for each leaf opportunity.

Mapping product hypotheses to experiments involves attaching riskiest assumptions to leaf opportunities and planning small tests. The skill outputs a Mermaid diagram that visually renders these explicit hypothesis tests alongside potential solutions for team communication.

Yes, you can generate multiple solution options for a single product opportunity. The skill proposes two to three concrete, scalable solutions with distinct scopes for each leaf opportunity within the hierarchical opportunity space.

Structuring product discovery from a top-down outcome requires defining a measurable target, then aligning multiple leaf opportunities beneath it. The skill supports this workflow by generating a tree with varied confidence levels and sources for each branch.

No dependencies are required to generate an opportunity solution tree. You simply describe your desired outcome, and the skill produces the full discovery structure with opportunities, solutions, experiments, and a rendered Mermaid diagram.

Use an opportunity solution tree when you need to validate problems and design testable experiments in product discovery. It maps outcomes to opportunities and explicit assumption tests, providing a hierarchical structure that a flat backlog lacks for managing confidence levels.